基于互近似熵和互样本熵的脑功能网络构建及应用

发布时间：2018-03-01 12:26

本文关键词： 脑功能网络脑电互近似熵互样本熵精神分裂症　出处：《西安电子科技大学》2014年硕士论文　论文类型：学位论文

【摘要】：精神分裂症是严重威胁人类健康的精神疾病，其根本原因在于它的发病机制至今还不清楚。近年来，脑功能网络被视为研究精神分裂症的有效方法，本文通过构建脑功能网络方法研究揭示静息状态下正常人和精神病患者脑网络特性、阳性症状和阴性症状的精神分裂症患者的复杂脑网络特性，得出一些有意义的结论。本文基于EEG的互近似熵和互样本熵来构建脑功能网络，首先采集14个正常受试者的32导脑电及48人精神分裂症患者的32导脑电，并利用小波包形成特定频率成分滤波器，对选取好的数据进行小波包分解，提取静息状态下14个正常人和14个精神分裂症患者的各导联脑电的、、、节律波并计算正常人和精神分裂症患者各节律波的互近似熵和互样本熵矩阵(32×32)，计算正常人和精神分裂症患者在不同阈值下的网络测度，对各个测度进行分析，结合阈值选取的原则选择合适阈值构建静息状态下正常人和精神分裂症患者的脑功能网络并对比分析。同理，对阳性症状和阴性症状的精神分裂症患者的脑功能网络特性进行分析。对于上述采集得到的14个正常受试者的32导脑电及48个精神分裂症患者(含阳性症状及阴性症状)的32导脑电，计算14个正常人、14个阳性症状精神分裂症患者和14个阴性症状精神分裂症患者的互信息矩阵(32×32)，，并计算正常人、阳性症状及阴性症状的精神分裂症患者在不同阈值下的网络测度，对各个测度进行分析，结合阈值选取的原则选择合适阈值构建正常人、阳性症状及阴性症状的精神分裂症患者的脑功能网络并进行对比分析。论文所提出的网络构建方法一定程度上能够刻画不同的脑功能状态，在分析、理解精神分裂症等精神疾病患者的发病机制等方面有可能成为一种新的有效方法。
[Abstract]:Schizophrenia is a serious threat to human health, the underlying cause is that its pathogenesis is still unclear. In recent years, brain functional networks have been regarded as an effective way to study schizophrenia. In this paper, the characteristics of brain network in normal and psychotic patients and the complex brain network characteristics of schizophrenic patients with positive and negative symptoms are revealed by the method of constructing brain functional network, and some meaningful conclusions are drawn. In this paper, we construct a brain functional network based on EEG's cross-approximation entropy and cross-sample entropy. Firstly, we collect the 32-lead EEG of 14 normal subjects and the 32-lead EEG of 48 schizophrenic patients, and use wavelet packets to form a filter with specific frequency components. The selected data is decomposed by wavelet packet, In the resting state, the EEG of 14 normal persons and 14 schizophrenic patients were extracted, the rhythmic waves were calculated, and the cross-approximate entropy and cross-sample entropy matrices of the rhythmic waves were calculated for normal and schizophrenic patients. Network measurements of schizophrenic patients at different thresholds, Each measure was analyzed and the appropriate threshold was selected according to the principle of threshold selection to construct the brain function network of normal and schizophrenic patients in resting state. In the same way, The characteristics of brain function network in schizophrenic patients with positive and negative symptoms were analyzed. The 32 conduction EEG and 48 schizophrenic patients (including positive symptoms and yin) were collected from 14 normal subjects. The 32-lead EEG of sexual symptoms, The mutual information matrix of 14 normal subjects, 14 positive symptomatic schizophrenia patients and 14 negative symptomatic schizophrenia patients were calculated, and the network measurements of normal subjects, positive and negative schizophrenic patients under different thresholds were calculated. According to the principle of threshold selection, the authors analyzed and analyzed the brain function network of schizophrenic patients with normal, positive and negative symptoms. The network construction method proposed in this paper can describe different brain function to a certain extent. It may become a new and effective method in analyzing and understanding the pathogenesis of schizophrenia and other mental diseases.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R749.3

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